We present a new universal transformer model, MCRT, together with a pre-training framework for predicting a wide range of physical properties for molecular crystals. We have designed a multi-modal architecture that has the capacity to comprehensively learn both local and global representations of molecular crystals. This ensures universal transferability for MCRT across different tasks and structures, at least for the tasks attempted here. We tested MCRT's predictive performance by fine-tuning it on various diverse properties such as lattice energy, methane capacity and diffusivity, as well as charge mobility. Our proposed model both outperformed current state-of-the-art models and showed strong generalisability performance in limited data availability scenarios. This highlights the practical utility in robustly accelerating materials discovery -for example, by rapidly estimating crystal structure prediction energy landscapes based on landscapes calculated for related molecules. Our MCRT model was also provided insights into structure-property relationships for molecular crystals through its permeable, interpretable architecture design. While such interpretability should not be equated to causal, physical understanding, it is striking that the attention scores in the MCRT model correlate so strongly with the key intermolecular interactions in lattice-energy prediction tasks, at least for molecules such as T2 that feature dominant hydrogen-bonding patterns (Fig. ,f; Figs. ).