Performance-based screening of porous materials for CO₂ capture and gas separation requires development of multiscale simulation workflows where physiochemical characteristics of adsorbents are obtained from molecular simulations, while separation performance of materials is evaluated at the process level by comparing overall energy efficiency and productivity in a particular process configuration. Practical implementation of these workflows requires: (a) accurate calculation of various material properties some of which are poorly estimated so far (e.g. specific heat capacity), (b) consistent treatment of the process variables that cannot be calculated from molecular simulations but are crucial for process modelling (e.g. pellet size and porosity), (c) improving computational efficiency of the workflows by reducing the search space in process optimization. In this study, we focus on four representative materials in the context of the vacuum swing adsorption process for carbon capture to probe these issues. We report on several observations with important implications for the theoretically achievable process efficiency, the computational efficiency of the multiscale workflows and on the consistency of materials rankings. We demonstrate that if size and porosity of adsorbent pellets are optimized, efficiency and productivity of the process can be substantially improved. We show the maximum performance of a material achievable in a particular process depends on a complex combination of both intrinsic material properties and process variables. This is evident from the ranking of the materials being different for a process with optimizable pellet size and porosity, compared to the reference case where these two properties are fixed. Analysis of the cycles on the Pareto fronts reveals common patterns for these variables for all the materials under consideration. We demonstrate that this observation reflects some optimum balance in the competition between diffusive processes into the pellet and convection flow processes across the bed. We attempt to capture this balance in a universal dimensionless metric which is explicitly proposed here for the first time. Application of such universal metrics could be very important in improving the efficiency of the optimization algorithms by narrowing down the multidimensional search space.