To the best of our knowledge, there is only one recent example demonstrating translation of the assembly and host:guest screening of MOCs into an automated mobile workflow, and the application of automated high-throughput screening and analysis methods to the MOC discovery process has not yet been thoroughly demonstrated. Additionally, most reported MOC studies only show a subset of successful reaction parameters, typically not including those that led to 'failures'. Often, the self-assembly results of reactions that either yielded the same structures, or indeed no stable structures, are not reported, despite providing valuable information for future work. The methods explored in this work will allow for the creation of a more complete picture of the self-assembly landscape. Not only will an automated workflow accelerate the experimental screening process, the creation of MOC self-assembly data in this way will be important for subsequent use in machine-learning (ML) methods towards MOC structure and property prediction. Uptake of ML methods using this type of data will underpin the future discovery of MOCs with valuable properties, to provide solutions for key industrial processes governed by host-guest binding.