I have a bunch of plots as the one reported below. The data is from measurements performed on different times and different days. In the plot (which is a cumulative distribution function, if that matters), the colors differentiate data relevant to different days; the markers are used to further differentiate the data within each day. The problem is that the plot is very crowded and a bit ugly. Some markers can be barely seen. Any idea how can I better plot this kind of data?

As you can see I tried to scatter the markers so that they would not appear in the same position. The code I used to create the markers (with python/matplotlib):

marker_list = ['$A$', '$B$', '$C$', '$D$', '$E$', '$F$', '$G$', '$H$', '$I$', '$J$',
               '$K$', '$L$', '$M$', '$N$', '$O$', '$P$', '$Q$', '$R$', '$S$', '$T$',
               '$U$', '$V$', '$W$', '$X$', '$Y$', '$Z$', r'$\Gamma$', r'$\Lambda$', 
               r'$\Omega$', r'$\Psi$',]
   
'set marker position'
rnd_lst = [np.random.randint(-10, 10) for i, _ in enumerate(marker_list)]
marker_pos = [[25+x, 45+x, 65+x, 87+x] for x in rnd_lst]    # fixed points + random number to scatter the markers

cc = plt.cycler(marker=marker_list, markevery=marker_pos)
plt.rc('axes', prop_cycle=cc)
plt.rc('lines', markersize=18)