MCRT performs well both in predicting properties across a broad range and in highlighting the top few 'best-performing' cystals (Figs. ). From a practical perspective, both of these tasks are important since there is very often a trade-off between different properties for real applications. To give just one example, to design materials for methane storage, we need to predict structures that have low lattice energies-that is, materials that will be formed in experiments-while having methane capacities that are high enough, rather than simply identifying the crystals that absorb the most methane. Both properties, lattice energy and methane capacity, are expensive to calculate. There are other important physical properties relevant to methane storage materials, not investgated here, such as mechanical stability and thermal conductivity, which would add further computational cost to a digital materials screening programme. As such, the development of universal, inexpensive prediction tools is a key priority in computational materials design. We believe that MCRT can serve as a foundational infrastructure for the molecular crystal research community, aiding us in the accelerated exploration of the vast space of molecular crystals.