Ok, I am trying to figure out which measure(s) would be the most meaningful in this case. ‘The way they cluster’ is not so simple to define.
At the moment I can think of 2 ways we can try:

We can run some clustering algorithm on your data in order to partition your cells into colonies. Then we can extract some features from the output (number of clusters, number of cells per cluster, intercluster distance, etc…). One problem with this though is that a ‘cluster’ is defined by the parameters we will provide to the algorithm. This can be fine since we will use the same parameters for all your data but these parameters are likely to influence your end result.

The other possibility is to use the Ripley’s K(t) functions. As I understand it, these functions allow to determine how far from a random distribution the cells are organised. It seems possible with this method to summarize the spatial patterns of the cells, compare the patterns in different conditions or even across distinct cell populations within one condition and test in each case for statistical significance.
I think the second option is what you really need. I need to do a bit of research to see if there exist an R package we can use maybe, we can use PickCells to segment and generate the point cloud to be used as input.
Let me know what you think.