For methane capacity (MC), a property with high computational screening cost, MCRT successfully predicts all the top ten best-performing structures, highlighting its potential for robustly accelerating highthroughput computational screening procedures using crystal structure prediction. Here this may be a better measure than MAE since we are mainly interested in the best-performing crystals. Additionally, for methane diffusivity (MD), which is challenging property to capture and predict using machine-learned interatomic potentials, MCRT still delivered the best predictive performance and successfully identified nine out of the top ten best-performing structures. When it comes to charge mobility (CM), a property with sparse data due to high computational cost, all models struggle somewhat with its prediction, but MCRT still identified seven out of the top ten structures with highest charge mobility. Finally, regarding the ∆-E task, MCRT captures more accurately the relative energy relationships across various structures. As shown in Fig. , the corrected energies by MCRT (turquoise points) align more closely with DFT-calculated results than those calculated by force fields (grey points), importantly in the low-energy regime, again successfully predicting all of the top ten structures.