In this paper, we aim to validate cDFT as a potential tool for adsorption property prediction of porous materials. We demonstrate the generalization of two primary features of cDFT previously mentioned in the literature: its low computational cost and minimal numerical uncertainty in the computation of the enthalpy of adsorption. These attributes position cDFT as a relevant tool for efficiently screening materials. The cost-effectiveness facilitates the screening of a large number of materials, while the low numerical uncertainty in enthalpies at high pressure enables robust temperature extrapolation of adsorption isotherms using the Clausius-Clapeyron relation, which is necessary for gradient-based optimization for process optimizations. However, these advantages come with a trade-off in accuracy, stemming from the discretization of adsorbate positions or the exclusion of adsorbate ori-entation as a degree of freedom during density profile optimization. Our study reveals that this loss in accuracy is negligible for CH 4 as the adsorbate but becomes more pronounced for N 2 and CO 2 . Understanding the materials prone to a larger deviation between cDFT and GCMC is crucial when applying cDFT to specific materials.